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The direct georeferencing accuracy of unmanned aerial vehicle (UAV) images with real-time kinematic (RTK) measurements is a concerned topic in the community of photogrammetry. This study assesses the capabilities of a multi-rotor platform equipped with RTK technology, specifically a DJI Phantom 4 RTK UAV, for robust direct georeferencing. The UAV surveyed a square and a building at Wuhan University to assess the accuracy and spatial consistency of direct georeferencing in close-range photography. We tested checkpoint errors under various ground control points (GCPs) configurations. The results show that without GCP, an analysis of 71 spatially distributed checkpoints produced a root mean square error (RMSE) of 5.58 cm in the Z direction. This finding indicates that RTK-equipped UAVs can achieve acceptable error margins even without using GCPs, thereby fulfilling the precision requirements for large-scale mapping.

Direct georeferencing uses onboard sensors to measure the position and orientation of the camera during image acquisition for photogrammetric applications. This approach aims to eliminate the use of traditional Ground Control Points (GCPs) in the photogrammetric process in order to reduce the costs and the time of the survey operations. The direct georeferencing technique involves integrating measurements from inertial measurement units (IMUs) and Global Navigation Satellite Systems (GNSS) data in order to evaluate the position and attitude of the camera with high accuracy (a few centimeters). In the Built Heritage survey domain, this approach is mainly followed by the employment of UAVs (Uncrewed aerial systems) platforms that are nowadays equipped with highly accurate systems able to evaluate the external parameters for the photogrammetric process. For terrestrial applications, few already achieved tests were performed; moreover, the sensors today available for extracting information from close-range acquisition systems are limited and sometimes under development. To evaluate the possibility offered by these new direct georeferencing tools, a test on the 3D ImageVector (REDcatch GmbH) has been performed. The results and the strategies followed will be presented and analyzed in order to understand better the accuracy and the potentiality of this new promising approach.

Unmanned aerial vehicle (UAV) has become an increasingly popular remote sensing platform in Antarctica. Due to the challenging natural conditions and lack of global navigation satellite system (GNSS) references in Antarctica, GNSS-supported direct georeferencing holds great potential for remote sensing applications in this region. Based on UAV surveys and GNSS observations we performed in Larsemann Hills, Antarctica, four GNSS-supported direct georeferencing schemes for UAV photogrammetry without ground control point (GCP) were designed and evaluated. Three of the schemes can generate high-accuracy photogrammetric products (horizontal accuracy: ∼0.7 ground sampling distance (GSD), vertical accuracy: ∼2.6 GSD). The fourth scheme, while exhibiting a lower accuracy at the meter-level, could offer high flexibility, and the accuracy of its derived products could be improved by post-flight transformation. The selection of an appropriate georeferencing scheme should be contingent upon the given application scenario, which can enhance the quality and accuracy of UAV photogrammetry in Antarctica. Potential applications of UAV remote sensing in Antarctica were discussed. It's proven that UAV photogrammetry constitutes a reliable tool for Antarctic expedition path planning and ice morphology evolution monitoring. Our study demonstrated that direct georeferencing can generate high-accuracy UAV products in a reliable and feasible way in Antarctica. [ABSTRACT FROM AUTHOR]

Drones offer a a unique survey platform that can operate below cloud cover and acquire very high spatial resolution datasets in near real-time. Studies have demonstrated that drones can be used for mapping over water using the Direct Georeferencing approach. However, this method is typically only feasible with high-end drones equipped with highly accurate GNSS/IMU systems. Moreover, placing targets over water to improve accuracy in post-processing can be challenging, further exacerbating this limitation. In this study, we developed an Assisted Direct Georeferencing method which combines the advantages of traditional Bundle Adjustment (BA) and Direct Georeferencing to overcome these challenges. Our approach utilizes BA over feature-rich segments of the drone trajectory, such as the shoreline, and DG in featureless areas, such as over water. To simulate a water-type environment or surface for our early tests, synthetic datasets have been created using Python for theoretical analysis. We then conducted a theoretical assessment of our approach under low and high variability attitude measurements. Our findings revealed that our methodology performs well under low variability attitude measurements, where wind conditions are close to optimal with an R-square value of 0.93. However, our model performs poorly under high variability attitude measurements, with an R-square value of only 0.028. These results suggest that Assisted Direct Georeferencing can serve as an alternative to high-end drones and Direct Georeferencing for water mapping applications in most standard. The findings from this theoretical assessment provide valuable insights into the achievable accuracy, error budgets, and limitations of the proposed model.

The spatial accuracy of unmanned aerial vehicles (UAVs) and the images they capture play a crucial role in the mapping process. Researchers are exploring solutions that use image-based techniques such as structure from motion (SfM) to produce topographic maps using UAVs while accessing locations with extremely high accuracy and minimal surface measurements. Advancements in technology have enabled real-time kinematic (RTK) to increase positional accuracy to 1–3 times the ground sampling distance (GSD). This paper focuses on post-processing kinematic (PPK) of positional accuracy to achieve a GSD or better. To achieve this, precise satellite orbits, clock information, and UAV global navigation satellite system observation files are utilized to calculate the camera positions with the highest positional accuracy. RTK/PPK analysis is conducted to improve the positional accuracies obtained from different flight patterns and altitudes. Data are collected at altitudes of 80 and 120 meters, resulting in GSD values of 1.87 cm/px and 3.12 cm/px, respectively. The evaluation of ground checkpoints using the proposed PPK methodology with one ground control point demonstrated root mean square error values of 2.3 cm (horizontal, nadiral) and 2.4 cm (vertical, nadiral) at an altitude of 80 m, and 1.4 cm (horizontal, oblique) and 3.2 cm (vertical, terrain-following) at an altitude of 120 m. These results suggest that the proposed methodology can achieve high positional accuracy for UAV image georeferencing. The main contribution of this paper is to evaluate the PPK approach to achieve high positional accuracy with unmanned aerial vehicles and assess the effect of different flight patterns and altitudes on the accuracy of the resulting topographic maps.

Unmanned Aircraft Systems (UAS) are used in a variety of applications with the aim of mapping detailed surfaces from the air. Despite the high level of map automation achieved today, there are still challenges in the accuracy of georeferencing that can limit both the speed and the efficiency in mapping urban areas. However, the integration of topographic grade Global Navigation Satellite System (GNSS) receivers on UAS has improved this phase, leading to a reach of up to a centimeter-level accuracy. It is therefore necessary to adopt direct georeferencing (DG), real-time kinematic positioning (RTK)/post-processed kinematic (PPK) approaches in order to largely automate the photogrammetric flow. This work analyses the positional accuracy using Ground Control Points (GCP) and the repeatability and reproducibility of photogrammetric products (Digital Surface Model and ortho-mosaic) of a commercial multi-rotor system equipped with a GNSS receiver in an urban environment with a DG approach. It was demonstrated that DG is a viable solution for mapping urban areas. Indeed, PPK with at least 1 GCP considerably improves the RMSE (x: 0.039 m, y: 0.012 m, and z: 0.034 m), allowing for a reliable 1:500 scale urban mapping in less time when compared to conventional topographic surveys.

Continuous monitoring of glaciers is of key importance to understand their morphological evolution over time and monitor the impact of climate change. Recently, Unmanned Aerial Vehicles (UAVs) have proven to be ideal candidates for glacier monitoring thanks to their flexibility and ease of processing with software packages. Traditionally, for high-accurate and geodetically relevant results, Ground Control Points (GCPs) need to be homogeneously distributed over the area of interest and manually identified in the imagery to guarantee accurate reconstructions. However, the GCP setup is always time consuming and, in many cases, a difficult operation due to logistic constraints. Nowadays, many UAVs offer GNSS Real Time Kinematic (RTK) capabilities that usually highly improve 3D reconstructions. However, there are circumstances in which an RTK solution cannot be directly achieved in the field. This is particularly frequent in challenging mountain environments such as glaciers. In such cases, post-processing UAV GNSS kinematic tracks could represent a powerful approach for improving the quality of 3D models. The goal of this work is to investigate the potential of UAV track post-processing combined with direct georeferencing for accurate 3D reconstructions without the need for GCPs in a complex environment of an Alpine glacier. The study area is Forni Glacier in the Rhaetian Alps, Italy. The data were acquired during two campaigns performed in August 2020 and August 2021 and include UAV images captured using a DJI Phantom 4 RTK and target positions measured with Leica GS18 I receivers. The data were processed using a pipeline entirely implemented in the Leica Infinity software that combines GNSS post-processing, a standard photogrammetric pipeline and a new tool to post-process GNSS kinematic tracks of UAVs. The approach based on UAV track post-processing and direct georeferencing was assessed using the acquired targets as Check Points (CPs) and compared to a standard photogrammetric approach in terms of glacier height loss computation. The results show Root Mean Square Errors (RMSEs) of the CPs below 4 cm for both the 2020 and 2021 campaigns. As for glacier height loss computation, the DPCs generated from the two surveys using a standard photogrammetric approach and a workflow based on UAV track post-processing and direct georeferencing were differentiated to compute the height differences of the glacier surfaces over one year. The two investigated approaches show similar results with an average height loss of 5 metres measured on the glacier tongue and demonstrate that UAV track post-processing can compensate for the RTK signal loss allowing accurate 3D reconstruction and eliminating the need for GCPs, especially if pre-calibration is performed.

Direct georeferencing of airborne mobile mapping systems is developing with unprecedented speed using GNSS/INS integration. Removal of systematic errors is required for achieving a high accurate georeferenced product in mobile mapping platforms with integrated GNSS/INS sensors. It is crucial to consider the deflection of verticals (DOV) in direct georeferencing due to the recently improved INS sensor accuracy. This study determines the DOV using Sweden’s EGM2008 model and gravity data. The influence of the DOVs on horizontal and vertical coordinates and considering different flight heights is assessed. The results confirm that the calculated DOV components using the EGM2008 model are sufficiently accurate for aerial photogrammetry purposes except for the mountainous areas because the topographic signal is not modeled correctly.

Drone technology has shown the potential to act as the middle ground between satellite, light aircraft, and terrestrial or in-situ methods. However, featureless terrain such as water poses a challenge when it comes to drone mapping. The main challenge is identifying matching points to combine overlapping images into a single dataset. In particular, because traditional methods such as Structure from Motion (SfM) is dependent on tie point collection, its usage over featureless terrain is almost impossible. In solving this problem, we propose that the use of Direct Georeferencing (DG) in registering images be explored as a potential method and we propose a method for correcting errors due to tilt with low-cost IMUs. This study first assesses the accuracy of direct georeferencing using low-cost Inertial Measurement Units (IMU) and Global Navigational Satellite System (GNSS) providing analysis of the error sources associated with direct georeferencing and then demonstrates new approaches to minimize them. To best simulate a water type environment or surface for the initial studies, a drone survey was conducted on flat farmland and a POSE analysis was performed. We then processed the images using direct georeferencing and then compared our error minimisation method to standard Bundle Block Adjustment with GCPs and again with no GCPs. Results showed that using the method proposed in this study helped reduce the Mean Absolute Error associated with direct georeferencing by 54%. These initial results show a clear potential for mapping over inland water using direct georeferencing.

Unmanned aerial vehicles (UAVs) can obtain high-resolution topography data flexibly and efficiently at low cost. However, the georeferencing process involves the use of ground control points (GCPs), which limits time and cost effectiveness. Direct georeferencing, using onboard positioning sensors, can significantly improve work efficiency. The purpose of this study was to evaluate the accuracy of the Global Navigation Satellite System (GNSS)-assisted UAV direct georeferencing method and the influence of the number and distribution of GCPs. A FEIMA D2000 UAV was used to collect data, and several photogrammetric projects were established. Among them, the number and distribution of GCPs used in the bundle adjustment (BA) process were varied. Two parameters were considered when evaluating the different projects: the ground-measured checkpoints (CPs) root mean square error (RMSE) and the Multiscale Model to Model Cloud Comparison (M3C2) distance. The results show that the vertical and horizontal RMSE of the direct georeferencing were 0.087 and 0.041 m, respectively. As the number of GCPs increased, the RMSE gradually decreased until a specific GCP density was reached. GCPs should be uniformly distributed in the study area and contain at least one GCP near the center of the domain. Additionally, as the distance to the nearest GCP increased, the local accuracy of the DSM decreased. In general, UAV direct georeferencing has an acceptable positional accuracy level.

Optical image sensors are the most common remote sensing data acquisition devices present in Unmanned Aerial Systems (UAS). In this context, assigning a location in a geographic frame of reference to the acquired image is a necessary task in the majority of the applications. This process is denominated direct georeferencing when ground control points are not used. Despite it applies simple mathematical fundamentals, the complete direct georeferencing process involves much information, such as camera sensor characteristics, mounting measurements, attitude and position of the UAS, among others. In addition, there are many rotations and translations between the different reference frames, among many other details, which makes the whole process a considerable complex operation. Another problem is that manufacturers and software tools may use different reference frames posing additional difficulty when implementing the direct georeferencing. As this information is spread among many sources, researchers may face difficulties on having a complete vision of the method. In fact, there is absolutely no paper in the literature that explain this process in a comprehensive way. In order to supply this implicit demand, this paper presents a comprehensive method for direct georeferencing of aerial images acquired by cameras mounted on UAS, where all required information, mathematical operations and implementation steps are explained in detail. Finally, in order to show the practical use of the method and to prove its accuracy, both simulated and real flights were performed, where objects of the acquired images were georeferenced.

To produce accurate topographic data, Unmanned Aerial Vehicle (UAV) still rely on Ground Control Points (GCPs) for georeferencing. However, using GCPs has several limitations, among others, related to the cost and time required for field measurements. In addition, not all areas are accessible for GCPs measurements due to poorly accessible terrain or security reasons. Direct georeferencing, a method to determine precise camera position and orientation in UAVs using Global Navigation Satellite System (GNSS) geodetic antenna. Post Processing Kinematic (PPK) or real-time coordinates can be applied to determine the camera position. One satellite that sends corrections to the rover on Earth is the Quasi-Zenith Satellite System (QZSS). This study aims to analyze the orthophoto accuracy of the results of direct georeferencing using precise coordinates from the QZSS satellites. The flight parameter was used at 60% sidelap and 80% overlap on an average flying altitude of 300 m above ground level resulting in 135 photos with a Ground Sampling Distance (GSD) value of 6 cm. The accuracy of direct georeferencing using QZSS horizontally and vertically was 1.134 m and 1.617 m, respectively. Meanwhile, the same metric results using conventional GCPs were 0.417 m horizontally and 0.419 m vertically. With these results, the horizontal accuracy of Direct Georeferencing using corrections from QZSS can be used for large-scale mapping of the 1: 5,000 class 1 scale, while vertical accuracy can be used for large-scale mapping of the 1: 5,000 class 3 scale. Direct georeferencing using QZSS corrections has the potential to support the acceleration of large-scale mapping activities in Indonesia. [ABSTRACT FROM AUTHOR]

Steep rock slopes present key opportunities and challenges within Earth science applications. Due to partial or complete inaccessibility, high-precision surveys of these high-relief landscapes remain a challenge. Direct georeferencing (DG) of unoccupied aerial vehicles (UAVs) with advanced onboard GNSS receivers presents opportunities to generate high-resolution 3D datasets without ground-based access to the study area. However, recent research has revealed large vertical errors using DG that may prove problematic in near-vertical terrain. To address these concerns, we examined more than 75 photogrammetric UAV-datasets with various imaging angles (nadir, oblique, and combinations) and ground control scenarios, including DG, along a steep slope exposure. Results demonstrate that mean errors in DG scenarios are up to 0.12 m higher than datasets using integrated georeferencing with well-distributed GCPs. Inclusion of GCPs greatly reduced mean error values but had limited influence on precision (

The georeferencing process is crucial to the accuracy of terrestrial laser scanner (TLS) data, in particular in the context of diachronic studies relying on multi-temporal surveys. The use of Ground Control Points in the georeferencing process can however be complex when confronted with the practical constraints of coastal surveying. A simple and quick alternative method called “pseudo-direct georeferencing” is proposed in the present paper. This method involves internal inclinometers to measure roll and pitch angles and a centimetric GPS to measure the position of the TLS center and the position of one backsight target. When assessing the transformational uncertainty by using a set of independent ground validation points for both classical indirect and proposed pseudo-direct methods, we respectively obtain root mean square errors of 4.4 cm for the indirect method and 3.8 cm for the pseudo-direct method.

Unmanned Aerial Vehicles (UAV) are enjoying increasing popularity in the photogrammetric community. The Chinese supplier DJI is the market leader with about 70% of the global consumer UAV market. The Phantom model has been available for more than 10 years and its current version "RTK" is equipped with a 2-frequency GNSS receiver, as a basis for direct georeferencing of image flights, using RTK or PPK technologies.In the context of the paper, different case studies are investigated, which allow statements on the geometric accuracy of UAV image flights as well as on the self-calibration of the camera systems.In the first example, four DJI Phantom 4 RTK systems are examined, which were flown in a cross flight pattern configuration on the area of the UAV test field "Zeche Zollern" in Dortmund, Germany. The second example analyses the results of an open moorland area where the establishment of GCPs is extremely difficult and expensive, hence direct georeferencing offers a promising way to evaluate deformations, soil movements or mass calculations. In this example a DJI Matrice 210 v2 RTK drone has been used and the results of two different software packages (Agisoft Metashape and RealityCapture) are analysed. The third example presents a reference building that has been established by the Lower Saxony administration for geoinformation in order to evaluate UAV photogrammetry for cadastral purposes. Here again a DJI Phantom 4 RTK has been tested in a variety of flight configurations and a large number of high precision ground control and check points.The case studies show that the RTK option leads to sufficient results if at least 1 GCP is introduced. Flights without any GCPs lead to a significant height error in the order of up to 30 GSD.

With the appearance of cost effective, easy to fly Unmanned Aerial Vehicles (UAV), a new type of data collection has been enabled: super high resolution multi-spectral, precisely georeferenced imagery and point clouds, collected over high value targets. The high spatial resolution and precise georeferencing accuracy makes information extraction and advanced analytics possible both in the spatial and temporal domain at scales simply not possible to collect from manned aircraft, and at much greater efficiency than can be collected from the ground. One example of this is plant phenotyping for experimental research where a high-accuracy spatial reference needs to be assigned to each plot entry to enable accurate and efficient plot level statistics of plant phenotypic attributes. This paper presents results from an integration of the Trimble APX-15-EI UAV Direct Georeferencing system with the Micasense Altum multi-spectral camera to produce a highly accurate and efficient UAV based mapping solution for advanced spatial and temporal analytics without the use of Ground Control Points (GCP’s). Results from a series of flights over a test range outfitted with GNSS surveyed check points show an orthomap accuracy at the level of 3 cm RMSx,y horizontal can be achieved. The same system flown over a test field operated by researchers at the University of Guelph containing plots of soybean demonstrated pixel-level alignment of the directly georeferenced orthomosaic to the cm-level plot boundaries previously surveyed by the researchers, thus meeting the requirements for automated phenotyping.

Recently, unmanned aerial vehicles (UAVs) have proven to be reliable and affordable alternatives to conventional manned-airplane aerial survey. UAV with small format rolling shutter camera that causes geometric distortion in image tends to suffer in accuracy, while uncertainty assessment mostly focuses on ground control point's and check point's accuracy, yet its impact on intrinsic parameters has not been fully explored. On the other hand, the advancement of small Global Navigation Satellite Systems (GNSS) and Inertial Measurement Units (IMU) have enabled direct georeferencing that doesn't require ground control points, or at least minimum number. The use of less accurate GNSS and IMU in UAV than conventional aerial survey complicates parameter estimation in such as camera calibration parameters, exterior orientation parameters, and so on, which requires attention on the combined impact with rolling shutter compensation option. In this study, the focus is on investigating the combined effect of rolling shutter and GNSS/IMU Georeferencing. Four sets of data with different direct georeferencing and rolling shutter option scenarios were processed using Agisoft Metashape professional. They were analyzed in terms of control/check points accuracy, boresight misalignment, camera calibration parameters, and exterior orientation parameters. The study showed that rolling shutter compensation plays a role in meeting conservative horizontal 1/2 pixels and vertical 1 pixel Root Mean Square Error (RMSE) criteria and that lens distortion residuals are reduced noticeably with rolling shutter option ON. The further investigation showed a coupling effect between intrinsic parameters and rolling shutter compensation effect with different direct georeferencing scenarios, which indicates a strong dependency among principal point offset, affinity, and tangential distortion. [ABSTRACT FROM AUTHOR]

The use of unmanned aerial vehicles (UAVs) is nowadays a standard approach in several application fields. Researches connected with these systems cover several topics and the evolution of these platforms and their applications are rapidly growing. Despite the high level of automatization reached nowadays, there is still a phase of the overall UAVs’ photogrammetric pipeline that requires a high effort in terms of time and resources (i.e., the georeferencing phase). However, thanks to the availability of survey-grade GNSS (Global Navigation Satellite System) receivers embedded in the aerial platforms, it is possible to also enhance this phase of the processing by adopting direct georeferencing approaches (i.e., without using any ground control point and exploiting real time kinematic (RTK) positioning). This work investigates the possibilities offered by a multirotor commercial system equipped with a RTK-enabled GNSS receiver, focusing on the accuracy of the georeferencing phase. Several tests were performed in an ad-hoc case study exploiting different georeferencing solutions and assessing the 3D positional accuracies, thanks to a network of control points. The best approaches to be adopted in the field according to accuracy requirements of the final map products were identified and operational guidelines proposed accordingly.

Airborne Light Detection and Ranging (LiDAR) system and digital camera are usually integrated on a flight platform to obtain multi-source data. However, the photogrammetric system calibration is often independent of the LiDAR system and performed by the aerial triangulation method, which needs a test field with ground control points. In this paper, we present a method for the direct georeferencing of images collected by a digital camera integrated in an airborne LiDAR system by automatic boresight misalignments calibration with the auxiliary of point cloud. The method firstly uses an image matching to generate a tie point set. Space intersection is then performed to obtain the corresponding object coordinate values of the tie points, while the elevation calculated from the space intersection is replaced by the value from the LiDAR data, resulting in a new object point called Virtual Control Point (VCP). Because boresight misalignments exist, a distance between the tie point and the image point of VCP can be found by collinear equations in that image from which the tie point is selected. An iteration process is performed to minimize the distance with boresight corrections in each epoch, and it stops when the distance is smaller than a predefined threshold or the total number of epochs is reached. Two datasets from real projects were used to validate the proposed method and the experimental results show the effectiveness of the method by being evaluated both quantitatively and visually.

UAVs platform are increasingly deployed by first responders and local stakeholders to get a first overview of disaster affected areas with a high level of detail and different off-nadir angle configurations. Through a rapid mapping approach, the acquired data (video sequences or pictures) are analysed to extract information on damages to buildings and infrastructures with the goal to support the Search and Rescue operations. The specific focus of the paper is on evaluating the expected benefits (from the rapid mapping perspective) deriving from a direct georeferencing approach when using UAV with RTK capabilities. Specifically, data acquired by a fixed wing eBee RTK platform by SenseFly over the areas affected by the earthquake that hit central Italy in 2016 have been processed to compare the positional accuracies of orthoimagery generated by means of a direct georeferencing approach (without any GPC) with and without a post-processing kinematic solution. The results highlight that an RTK-enabled platform allows to achieve orthoimagery positioning accuracy values up to few centimeters without the need of any control point. In the conclusion session the operational implications of a PPK-based approach versus a standard direct georeferencing are critically discussed.

The spatial accuracy of unmanned aerial vehicles (UAVs) and the images they capture play a crucial role in the mapping process. Researchers are exploring solutions that use image-based techniques such as structure from motion (SfM) to produce topographic maps using UAVs while accessing locations with extremely high accuracy and minimal surface measurements. Advancements in technology have enabled real-time kinematic (RTK) to increase positional accuracy to 1–3 times the ground sampling distance (GSD). This paper focuses on post-processing kinematic (PPK) of positional accuracy to achieve a GSD or better. To achieve this, precise satellite orbits, clock information, and UAV global navigation satellite system observation files are utilized to calculate the camera positions with the highest positional accuracy. RTK/PPK analysis is conducted to improve the positional accuracies obtained from different flight patterns and altitudes. Data are collected at altitudes of 80 and 120 meters, resulting in GSD values of 1.87 cm/px and 3.12 cm/px, respectively. The evaluation of ground checkpoints using the proposed PPK methodology with one ground control point demonstrated root mean square error values of 2.3 cm (horizontal, nadiral) and 2.4 cm (vertical, nadiral) at an altitude of 80 m, and 1.4 cm (horizontal, oblique) and 3.2 cm (vertical, terrain-following) at an altitude of 120 m. These results suggest that the proposed methodology can achieve high positional accuracy for UAV image georeferencing. The main contribution of this paper is to evaluate the PPK approach to achieve high positional accuracy with unmanned aerial vehicles and assess the effect of different flight patterns and altitudes on the accuracy of the resulting topographic maps. [ABSTRACT FROM AUTHOR]

The use of UAV-based laser scanning systems is increasing due to the rapid development in sensor technology, especially in applications such as topographic surveys or forestry. One advantage of these multi-sensor systems is the possibility of direct georeferencing of the derived 3D point clouds in a global reference frame without additional information from Ground Control Points (GCPs). This paper addresses the quality analysis of direct georeferencing of a UAV-based laser scanning system focusing on the absolute accuracy and precision of the system. The system investigated is based on the RIEGL miniVUX-SYS and the evaluation uses the estimated point clouds compared to a reference point cloud from Terrestrial Laser Scanning (TLS) for two different study areas. The precision is estimated by multiple repetitions of the same measurement and the use of artificial objects, such as targets and tables, resulting in a standard deviation of

Unmanned aerial systems (UAS) and structure-from-motion photogrammetry are transforming the way we produce topographic data, with applications covering many disciplines in the geosciences, including coastal studies. To overcome limitations of ground control points (GCPs), we evaluate direct georeferencing (DG) of consumer UAS imagery for the cost-effective measurement of beach topography. Using DG, camera positions determined with on-board instruments provide air control points for photogrammetry, obviating the need for presurveyed GCPs. We validate the approach at Orewa Beach, New Zealand, achieving vertical accuracies similar to light detection and ranging (< 0.2 m) at a higher resolution (< 0.1 m). A low-quality global navigation satellite system onboard a consumer UAS remains the main constraint on measurement quality. We show how independent topographic data sets, which are increasingly available worldwide, can improve measurement quality, and hence change detection capacity. Our understanding of measurement quality achieved in this study is applied to the assessment of morphological and volumetric change at Orewa Beach. [ABSTRACT FROM AUTHOR]

Unlike mobile survey systems, stationary survey systems are given very little direct georeferencing attention. Direct Georeferencing is currently being used in several mobile applications, especially in terrestrial and airborne LiDAR systems. Georeferencing of stationary terrestrial LiDAR scanning data, however, is currently performed indirectly through using control points in the scanning site. The indirect georeferencing procedure is often troublesome; the availability of control stations within the scanning range is not always possible. Also, field procedure can be laborious and involve extra equipment and target setups. In addition, the conventional method allows for possible human error due to target information bookkeeping. Additionally, the accuracy of this procedure varies according to the quality of the control used. By adding a dual GPS antenna apparatus to the scanner setup, thereby supplanting the use of multiple ground control points scattered throughout the scanning site, we mitigate not only the problems associated with indirect georeferencing but also induce a more efficient set up procedure while maintaining sufficient precision. In this paper, we describe a new method for determining the 3D absolute orientation of LiDAR point cloud using GPS measurements from two antennae firmly mounted on the optical head of a stationary LiDAR system. In this paper, the general case is derived where the orientation angles are not small; this case completes the theory of stationary LiDAR direct georeferencing. Simulation and real world field experimentation of the prototype implementation suggest a precision of about 0.05 degrees (~1 milli-radian) for the three orientation angles.

The Trimble Aerial Camera x4 (i.e., TACx4) is a photogrammetric multi-head system manufactured by Trimble Inc.© in 2010. It has four cameras mounted together in the main structure allowing the simultaneous acquisition to generate a single synthetic image with much larger ground coverage. In addition, the cameras are also integrated with a GNSS/INS to perform “Direct” or “Integrated” Sensor Orientation. The main condition to obtain photogrammetric mapping products with high accuracy using a direct sensor orientation procedure is to execute a step known as “geometric system calibration”. In general, the photogrammetric multi-head system manufacturers perform this step using laboratory methods to obtain the parameters of cameras interior and relative orientation. Accurate mounting parameters (lever arms and “boresight misalignments”) are fundamental requirements to generate the synthetic image when georeferencing of images is applied. This paper shows a “full field” calibration method to perform the geometric system calibration of the TACx4 system and its evaluation for direct sensor orientation mapping applications. The developed method involves two steps using only aerial images: (1) estimation of the cameras interior and relative orientation parameters to generate the synthetic image and (2) estimation of the synthetic image interior orientation and the mounting parameters between the synthetic image and GNSS/INS reference systems using two different methods. The obtained results in the conventional photogrammetric project show that the proposed method allows performing the geometric system calibration of the TACx4 system achieving around 50 cm (5 pixels) in horizontal and vertical accuracies. The obtained results can be used for large-scale mapping requirements using direct sensor orientation according to Brazilian accuracy standards.

Within the past years, the development of high-quality Inertial Measurement Units (IMU) and GNSS technology and dedicated RTK (Real Time Kinematic) and PPK (Post-Processing Kinematic) solutions for UAVs promise accurate measurements of the exterior orientation (EO) parameters which allow to georeference the images. Whereas the positive impact of known precise GNSS coordinates of camera positions is already well studied, the influence of the angular observations have not been studied in depth so far. Challenges include accuracies of GNSS/IMU observations, excessive angular motion and time synchronization problems during the flight. Thus, this study assesses the final geometric accuracy using direct georeferencing with high-quality post-processed IMU/GNSS and PPK corrections. A comparison of different data processing scenarios including indirect georeferencing, integrated solutions as well as direct georeferencing provides guidance on the workability of UAV mapping approaches that require a high level of positional accuracy. In the current research the results show, that the use of the post-processed APX-15 GNSS and IMU data was particularly beneficial to enhance the image orientation quality. Horizontal accuracies within the pixel level (2.8 cm) could be achieved. However, it was also shown, that the angular EO parameters are still too inaccurate to be assigned with a high weight during the image orientation process. Furthermore, detailed investigations of the EO parameters unveil that systematic sensor misalignments and offsets of the image block can be reduced by the introduction of four GCPs. In this regard, the use of PPK corrections reduces the time consuming field work to measure high quantities of GCPs and makes large-scale UAV mapping a more feasible solution for practitioners that require high geometric accuracies.